EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary Computation

Inspired by natural evolutionary processes, Evolutionary Computation (EC) has established itself as a cornerstone of Artificial Intelligence. EC possesses distinctive attributes, including adaptability and the capability to explore expansive problem spaces, making it invaluable in domains that require intricate black-box optimization. Recently, with the surge in data-intensive applications and large-scale complex systems, the demand for scalable EC solutions has grown significantly. However, many existing EC libraries, which were originally designed for modest scales, fall short in catering to the heightened demands of modern problems. While the advent of some pioneering GPU-accelerated EC libraries is a step forward, they too grapple with limitations, particularly in terms of flexibility and architectural robustness. To address these limitations, we introduce EvoX: a computing framework tailored for automated, distributed, and heterogeneous execution of EC algorithms. At the core of EvoX lies a functional programming model that simplifies the development of parallelized EC algorithms, seamlessly integrated with a high-performance computation model designed specifically for distributed GPU-accelerated execution. Building upon foundation, we have crafted an extensive library comprising a wide spectrum of 45 EC algorithms for both single- and multi-objective optimization. Furthermore, the library offers comprehensive support for a diverse set of benchmark problems, ranging from dozens of numerical test functions to hundreds of neuroevolution and reinforcement learning tasks/environments. Through extensive experiments across a range of problem scenarios and hardware configurations, EvoX has demonstrated robust system and model performances. EvoX is open-source and accessible at: https://github.com/EMI-Group/EvoX.